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Record W4366463630 · doi:10.1177/10790632231170818

Sexual Misconduct: What Does a 20-Year Review of Cases in Quebec Reveal about the Characteristics of Professionals, Victims, and the Disciplinary Process?

2023· review· en· W4366463630 on OpenAlexaboutno aff
Geneviève Martin, Isabelle Beaulieu

Bibliographic record

VenueSexual Abuse · 2023
Typereview
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSexual misconductMisconductDisciplineRecidivismSexual intercoursePsychologyMedicineCriminologyClinical psychologyPsychiatryPolitical scienceLaw

Abstract

fetched live from OpenAlex

While there is a considerable body of literature on sexual aggression, we know much less about the violation of sexual boundaries within professional relationships. To address this knowledge gap, the characteristics of cases of sexual misconduct in the province of Quebec were extracted, based on a search of published disciplinary decisions between 1998 and 2020, using the legal databases CANLII and SOQUIJ. The search yielded 296 decisions including 249 male and 47 female members from 22 professional orders, and involving 470 victims. Results indicate that male professionals approaching mid-career accounted for a greater proportion of cases of sexual misconduct. Moreover, physical and mental health professionals were overrepresented in cases, as were female adult victims. Acts of sexual misconduct concerned mostly sexual touching and intercourse and occurred during consultations. Female professionals were more inclined to establish romantic and sexual relationships with clients than their male counterparts. Of the 92.0% of professionals found guilty of at least one count of sexual misconduct, two thirds eventually returned to practice. Following the guilty verdict, few faced rehabilitative measures. Recommendations are provided for the prevention of sexual recidivism and the accompaniment of victims of sexual misconduct throughout the disciplinary process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.528
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.423
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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